• The Journal of the Acoustical Society of Korea
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• v.16 no.5
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• pp.112-121
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• 1997

In this paper, we study a model parameter compensation method for noise-robust speech recognition. We study model parameter compensation on a sentence by sentence and no other informations are used. Parallel model combination(PMC), well known as a model parameter compensation algorithm, is implemented and used for a reference of performance comparision. We also propose a modified PMC method which tunes model parameter with an association factor that controls average variability of gaussian mixtures and variability of single gaussian mixture per state for more robust modeling. We obtain a re-estimation solution of environmental variables based on the expectation-maximization(EM) algorithm in the cepstral domain. To evaluate the performance of the model compensation methods, we perform experiments on speaker-independent isolated word recognition. Noise sources used are white gaussian and driving car noise. To get corrupted speech we added noise to clean speech at various signal-to-noise ratio(SNR). We use noise mean and variance modeled by 3 frame noise data. Experimental result of the VTS approach is superior to other methods. The scheme of the zero order VTS approach is similar to the modified PMC method in adapting mean vector only. But, the recognition rate of the Zero order VTS approach is higher than PMC and modified PMC method based on log-normal approximation.

• MALSORI
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• no.50
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• pp.51-69
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• 2004

In this paper, we present a one-pass semi-dynamic network decoding framework that inherits both advantages of fast decoding speed from static network decoders and memory efficiency from dynamic network decoders. Our method is based on the novel language model network representation that is essentially of finite state machine (FSM). The static network derived from the language model network [1][2] is partitioned into smaller subnetworks which are static by nature or self-structured. The whole network is dynamically managed so that those subnetworks required for decoding are cached in memory. The network is near-minimized by applying the tail-sharing algorithm. Our decoder is evaluated on the 25k-word Korean broadcast news transcription task. In case of the search network itself, the network is reduced by 73.4% from the tail-sharing algorithm. Compared with the equivalent static network decoder, the semi-dynamic network decoder has increased at most 6% in decoding time while it can be flexibly adapted to the various memory configurations, giving the minimal usage of 37.6% of the complete network size.

• Speech Sciences
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• v.7 no.4
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• pp.49-57
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• 2000

This paper proposes an effective decision method focused on evaluating the 'stress position'. Conventional methods usually extract the acoustic parameters and compare them to references in absolute scale, adversely producing unstable results as testing conditions change. To cope with environmental dependency, the proposed method is designed to be model-based and determines the stressed interval by making relative comparison over candidates. The stressed/unstressed models are then induced from normal phone models by adaptive training. The experimental results indicate that the proposed method is promising, and that it is useful for automatic detection of stress positions. The results also show that generating the stressed/unstressed model by adaptive training is effective.

• The Journal of the Acoustical Society of Korea
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• v.37 no.6
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• pp.506-511
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• 2018

This paper proposes an GMM(Gaussian Mixture Model)-DNN(Deep Neural Network) hybrid-based feature compensation method for effective speech recognition in noisy environments. In the proposed algorithm, the posterior probability for the conventional GMM-based feature compensation method is calculated using DNN. The experimental results using the Aurora 2.0 framework and database demonstrate that the proposed GMM-DNN hybrid-based feature compensation method shows more effective in Known and Unknown noisy environments compared to the GMM-based method. In particular, the experiments of the Unknown environments show 9.13 % of relative improvement in the average of WER (Word Error Rate) and considerable improvements in lower SNR (Signal to Noise Ratio) conditions such as 0 and 5 dB SNR.

• MALSORI
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• no.63
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• pp.101-112
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• 2007

Spoken dialog system development includes many laborious and inefficient tasks. Since there are many components such as speech recognition, language understanding, dialog management and knowledge management in a spoken dialog system, a developer should take an effort to edit corpus and train each model separately. To reduce a cost for editing corpus and training each model, we need more systematic and efficient working environment. For the working environment, we propose DialogStudio as a spoken dialog system workbench.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.268-282
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• 2000

Speech is one of the most efficient communication media and it includes several kinds of factors about speaker, context emotion and so on. Human emotion is expressed in the speech, the gesture, the physiological phenomena(the breath, the beating of the pulse, etc). In this paper, the method to have cognizance of emotion from anyone's voice signals is presented and simulated by using neuro-fuzzy model.

• MALSORI
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• no.45
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• pp.93-105
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• 2003

In this paper, we present a single framework based on GMM for speaker recognition. The proposed framework can simultaneously minimize environmental variations on mismatched conditions and adapt the bias free and speaker-dependent characteristics of claimant utterances to the background GMM to create a speaker model. We compare the closed-set speaker identification for conventional method and the proposed method both on TIMIT and NTIMIT. In the several sets of experiments we show the improved recognition rates on a simulated channel and a telephone channel condition by 7.2% and 27.4% respectively.

• Proceedings of the IEEK Conference
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• summer
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• pp.92-96
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• 2004

The researches of the emotions are currently great interest in speech processing as well as in human-machine interaction domain. In the recent years, more and more of researches relating to emotion synthesis or emotion recognition are developed for the different purposes. Each approach uses its methods and its various parameters measured on the speech signal. In this paper, we proposed using a short-time parameter: MFCC coefficients (Mel­Frequency Cepstrum Coefficients) and a simple but efficient classifying method: Vector Quantification (VQ) for speaker-dependent emotion recognition. Many other features: energy, pitch, zero crossing, phonetic rate, LPC... and their derivatives are also tested and combined with MFCC coefficients in order to find the best combination. The other models: GMM and HMM (Discrete and Continuous Hidden Markov Model) are studied as well in the hope that the usage of continuous distribution and the temporal behaviour of this set of features will improve the quality of emotion recognition. The maximum accuracy recognizing five different emotions exceeds $88\%$ by using only MFCC coefficients with VQ model. This is a simple but efficient approach, the result is even much better than those obtained with the same database in human evaluation by listening and judging without returning permission nor comparison between sentences [8]; And this result is positively comparable with the other approaches.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.12
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• pp.1203-1208
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• 2009

Green IT based u-City means that u-City having Green IT concept. If we adopt the situation awareness or not, the processing of Green IT may be reduced. For example, if we recognize a lot of speech sound on CCTV in u-City environment, it takes a lot of processing time and cost. However, if we want recognize emergency sound on CCTV, it takes a few reduced processing cost. So, for detecting emergency state dynamically through CCTV, we propose our advanced speech recognition system. For the purpose of that, we adopt HMM (Hidden Markov Model) for feature extraction. Also, we adopt Wiener filter technique for noise elimination in many information coming from on CCTV in u-City environment.

• MALSORI
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• no.53
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• pp.143-156
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• 2005

Speaker adaptation in eigenvoice space is a popular method for rapid speaker adaptation. To improve the performance of the method, the number of speaker dependent models should be increased and eigenvoices should be re-estimated. However, principal component analysis takes much time to find eigenvoices, especially in a continuous speech recognition system. This paper describes a method to reduce computation time to estimate eigenvoices only for supplementary speaker dependent models and to merge them with the used eigenvoices. Experiment results show that the computation time is reduced by 73.7% while the performance is almost the same in case that the number of speaker dependent models is the same as used ones.